University of Vienna

About: University of Vienna is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Population & Context (language use). The organization has 44686 authors who have published 95840 publications receiving 2907492 citations.

Linear Discrimination, Ordination, and the Visualization of Selection Gradients in Modern Morphometrics

Abstract: Linear discriminant analysis (LDA) is a multivariate classification technique frequently applied to morphometric data in various biomedical disciplines. Canonical variate analysis (CVA), the generalization of LDA for multiple groups, is often used in the exploratory style of an ordination technique (a low-dimensional representation of the data). In the rare case when all groups have the same covariance matrix, maximum likelihood classification can be based on these linear functions. Both LDA and CVA require full-rank covariance matrices, which is usually not the case in modern morphometrics. When the number of variables is close to the number of individuals, groups appear separated in a CVA plot even if they are samples from the same population. Hence, reliable classification and assessment of group separation require many more organisms than variables. A simple alternative to CVA is the projection of the data onto the principal components of the group averages (between-group PCA). In contrast to CVA, these axes are orthogonal and can be computed even when the data are not of full rank, such as for Procrustes shape coordinates arising in samples of any size, and when covariance matrices are heterogeneous. In evolutionary quantitative genetics, the selection gradient is identical to the coefficient vector of a linear discriminant function between the populations before vs. after selection. When the measured variables are Procrustes shape coordinates, discriminant functions and selection gradients are vectors in shape space and can be visualized as shape deformations. Except for applications in quantitative genetics and in classification, however, discriminant functions typically offer no interpretation as biological factors.

Pareto Ant Colony Optimization: A metaheuristic approach to multiobjective portfolio selection

Abstract: Selecting the “best” project portfolio out of a given set of investment proposals is a common and often critical management issue. Decision-makers must regularly consider multiple objectives and often have little a priori preference information available to them. Given these contraints, they can improve their chances of achieving success by following a two-phase procedure that first determines the solution space of all efficient (i.e., Pareto-optimal) portfolios and then allows them to interactively explore that space. However, the task of determining the solution space is not trivial: brute-force complete enumeration only works for small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. Meta-heuristics provide a useful compromise between the amount of computation time necessary and the quality of the approximated solution space. This paper introduces Pareto Ant Colony Optimization as an especially effective meta-heuristic for solving the portfolio selection problem and compares its performance to other heuristic approaches (i.e., Pareto Simulated Annealing and the Non-Dominated Sorting Genetic Algorithm) by means of computational experiments with random instances. Furthermore, we provide a numerical example based on real world data.

Natalizumab treatment for multiple sclerosis: updated recommendations for patient selection and monitoring

Abstract: Natalizumab, a highly specific α4-integrin antagonist, is approved for treatment of patients with active relapsing-remitting multiple sclerosis (RRMS). It is generally recommended for individuals who have not responded to a currently available first-line disease-modifying therapy or who have very active disease. The expected benefits of natalizumab treatment have to be weighed against risks, especially the rare but serious adverse event of progressive multifocal leukoencephalopathy. In this Review, we revisit and update previous recommendations on natalizumab for treatment of patients with RRMS, based on additional long-term follow-up of clinical studies and post-marketing observations, including appropriate patient selection and management recommendations.

Aged mother cells of Saccharomyces cerevisiae show markers of oxidative stress and apoptosis

Abstract: Recently, we and others have shown that genetic and environmental changes that increase the load of yeast cells with reactive oxygen species (ROS) lead to a shortening of the life span of yeast mother cells. Deletions of yeast genes coding for the superoxide dismutases or the catalases, as well as changes in atmospheric oxygen concentration, considerably shortened the life span. The presence of the physiological antioxidant glutathione, on the other hand, increased the life span of yeast cells. Taken together, these results pointed to a role for oxygen in the yeast ageing process. Here, we show by staining with dihydrorhodamine that old yeast mother cells isolated by elutriation, but not young cells, contain ROS that are localized in the mitochondria. A relatively large proportion of the old mother cells shows phenotypic markers of yeast apoptosis, i.e. TUNEL (TdT-mediated dUTP nick end labelling) and annexin V staining. Although it has been shown previously that apoptosis in yeast can be induced by a cdc48 allele, by expressing pro-apoptotic human cDNAs or by stressing the cells with hydrogen peroxide, we are now showing a physiological role for apoptosis in unstressed but aged wild-type yeast mother cells.